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Likelihood Ratio, Wald, and Lagrange Multiplier (Score) Tests Soccer Goals in European Premier Leagues - 2004.

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Presentation on theme: "Likelihood Ratio, Wald, and Lagrange Multiplier (Score) Tests Soccer Goals in European Premier Leagues - 2004."— Presentation transcript:

1 Likelihood Ratio, Wald, and Lagrange Multiplier (Score) Tests Soccer Goals in European Premier Leagues

2 Statistical Testing Principles Goal: Test a Hypothesis concerning parameter value(s) in a larger population (or nature), based on observed sample data Data – Identified with respect to a (possibly hypothesized) probability distribution that is indexed by one or more unknown parameters Notation:

3 Example – English League – Total Goals/Match Suppose we wish to test whether the mean number of goals (in a hypothetically infinite population) of games is equal to 3. Note: all games of equal length (no overtime in regular season games) Data: Y=Total # of goals in a randomly selected game Distribution: Assume Poisson with parameter  Null Hypothesis: H 0 :  = 3 Alternative Hypothesis: H A :  ≠ 3 Joint Probability Density Function:

4 Likelihood Function Another term for joint probability density/mass function. Common Notation: L(  ) or L( ,y) or L(  |y) Considered as a function of both the (observed) data and the (unknown) parameter values Used in estimation and testing parameter value(s) Goal is to choose parameter value(s) that maximize likelihood function given the observed data. Typically work with the log of the likelihood, as it is often easier to differentiate to solve for maximum likelihood (ML) estimators for many families of probability distributions

5 ML Estimation of Poisson Mean

6 Total Goals Data


8 Likelihood Ratio Test Identify the parameter space:  Identify the parameter space under H 0 :      Evaluate the maximum log-Likelihood Evaluate the log-Likelihood under H 0 Any terms not involving parameter can be ignored Take -2 times difference (H 0 – maximum) Under null hypothesis (and large samples), statistic is approximately chi-square with 1 degree of freedom (number of constraints under H 0 )

9 Soccer Goals Example We have strong evidence to conclude the “true” mean total number of goals is below 3.

10 Wald Test - I By Central Limit Theorem arguments, many estimators have sampling distributions that are approximately normal in large samples Then, if we have an estimate of the variance of the estimator, we can obtain a chi-square statistic by taking the square of the distance between the ML estimate and the value under H 0 divided by the estimated variance The estimated variance can be obtained from the second derivative of the log-Likelihood

11 Wald Test - II

12 Lagrange Multiplier (Score) Test Obtain the first derivative of the log-Likelihood evaluated at the parameter under H 0 (This is the slope of the log-Likelihood, evaluated at  0 and is called the score) Multiply the square of the score by the variance of the ML estimate, evaluated at  0. This is the inverse of the variance of the score. Then chi-square test statistic is computed as follows:

13 Soccer Goals Example


15 Generalization to Tests of Multiple Parameters

16 Soccer Goals Example Premier League Games in 2004 for k=5 European Countries:  England n 1 = 380, Y 1 = 975  France n 2 = 380, Y 2 = 826  Germany n 3 = 306, Y 3 = 890  Italy n 4 = 380, Y 4 = 960  Spain n 5 = 380, Y 5 = 980

17 Testing Equality of Mean Goals Among Countries - I

18 Testing Equality of Mean Goals Among Countries - II

19 Likelihood Ratio Test Evidence that the true population means differ (in particular: France lower, Germany higher than the others)

20 Wald Test

21 Lagrange Multiplier (Score) Test

22 Testing Goodness of Fit to Poisson Distribution All estimation and testing has assumed that number of goals follow Poisson distributions To test whether that assumption is reasonable, we compare the observed distributions of goals with what we would expect under the Poisson model We can check whether the observed mean and variance are similar (under Poisson model they are equal) We can also obtain a chi-square statistic by summing over range of goals: (observed#-expected#) 2 /expected# which under hypothesis of model fits is approximately chi-square with (# in range)-1 degrees of freedom

23 Distributions of Goals All leagues, except France, appear to be well described by the Poisson distribution. Especially England, Germany, and Spain

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